Self-organization based clustering scheme for FANETs using Glowworm Swarm Optimization

Khan, Asif; Aftab, Farooq; Zhang, Zhongshan

doi:10.1016/j.phycom.2019.100769

Cited by 67 publications

(62 citation statements)

References 24 publications

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“…The KH algorithm is used for predicting cluster management. Khan et al [45] proposed a self-organized-based clustering scheme (SOCS) for FANET. SOCS is based on GSO; the GSO algorithm is used for the cluster formation and management phases.…”

Section: Review Of Related Studiesmentioning

confidence: 99%

Bio-Inspired Approaches for Energy-Efficient Localization and Clustering in UAV Networks for Monitoring Wildfires in Remote Areas

Arafat

Moh

2021

IEEE Access

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In dynamic unmanned aerial vehicle (UAV) networks, localization and clustering are fundamental functions for cooperative control. In this article, we propose bio-inspired localization (BIL) and clustering (BIC) schemes in UAV networks for wildfire detection and monitoring. First, we develop a hybrid gray wolf optimization (HGWO) method and propose an energy-efficient three-dimensional BIL algorithm based on the HGWO, which reduces localization errors, avoids flip ambiguity in bounded distance measurement errors, and achieves high localization accuracy. In BIL, the bounding cube method is applied to reduce the initial search space. Second, we propose an energy-efficient BIC algorithm based on the HGWO. The BIC algorithm utilizes the gray wolf leadership hierarchy to improve clustering efficiency. We also develop an analytical model for determining the optimal number of clusters that provide the minimum number of transmissions. Finally, we propose a GWO-based compressive sensing (CS-GWO) algorithm to transmit data from cluster heads (CHs) to the base station (BS). The proposed CS-GWO constructs an efficient routing tree from CHs to the BS, thereby reducing the routing delay and the number of transmissions. Our performance evaluation shows that the proposed BIL and BIC significantly outperform conventional schemes in terms of various performance metrics under different scenarios.INDEX TERMS Bio-inspired algorithm, cluster head, clustering, energy efficiency, gray wolf optimization, localization, network lifetime, routing protocol, unmanned aerial vehicle network.

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Section: Review Of Related Studiesmentioning

confidence: 99%

Bio-Inspired Approaches for Energy-Efficient Localization and Clustering in UAV Networks for Monitoring Wildfires in Remote Areas

Arafat

Moh

2021

IEEE Access

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“…Also, different transmission power levels are defined for which is distance dependent to save the extra drainage of power. A clustering technique based on Glowworm Swarm Optimization is presented for FANETs [16]. The clustering scheme is a selforganization based where cluster formation take place depending upon the connectivity to the ground station and the residual energy.…”

Section: Related Workmentioning

confidence: 99%

Statistical Analysis of Optimization-Based Clustering Scheme for Multi-UAV Networks

Kumar¹,

Goswami²,

Arya³

2020

MMEP

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The advancement in the widespread application of multi-UAV (Unmanned Aerial Vehicle) systems introduces certain design challenges in terms of communication and network stability. In this paper, addressing these design aspects, a clustering scheme is presented. The paper aims to improve the lifetime of a Flying Ad-hoc Network (FANET) considering the node distances, residual energy, delay and coverage as the fundamental elements for the election of the cluster heads and simultaneous formation of clusters. The clustering scheme is carried out by four different well-known metaheuristic techniques, i.e., Water Cycle Algorithm (WCA), Crow Search Algorithm (CSA), Firefly Algorithm (FA) and the Cuckoo Search Algorithm (CuSA). In addition, the topology of the network is maintained with periodic updating of the positions of the UAV nodes. Furthermore, the performance of these techniques is evaluated and discussed in terms of the time required for the cluster formation, network energy consumption, alive node analysis and finally, the network lifetime. Rigorous simulations are then carried out for different network areas with varying node densities. The comparative analysis demonstrates a significantly improved performance of the Crow Search based scheme for clustering over the other implemented techniques.

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“…Artificial intelligence (AI) approaches, including artificial neural network [95], fuzzy logic [96], reinforcement learning [97], and particle swarm optimization [98], has been adopted to improve the performance of complex systems in a diverse range of applications, such as FANETs. In FANETs, AI has been applied in routing [99] so that a decision maker observes the state of the operating environment, selects the best possible action (e.g., a stable and reliable route), receives reward, and learns about the appropriateness of the action under the state based on the reward.…”

Section: What Are the Roles Of Artificial Intelligence In Fanets?mentioning

confidence: 99%

Routing Schemes in FANETs: A Survey

Khan

Yau

Noor

et al. 2019

Sensors

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Flying ad hoc network (FANET) is a self-organizing wireless network that enables inexpensive, flexible, and easy-to-deploy flying nodes, such as unmanned aerial vehicles (UAVs), to communicate among themselves in the absence of fixed network infrastructure. FANET is one of the emerging networks that has an extensive range of next-generation applications. Hence, FANET plays a significant role in achieving application-based goals. Routing enables the flying nodes to collaborate and coordinate among themselves and to establish routes to radio access infrastructure, particularly FANET base station (BS). With a longer route lifetime, the effects of link disconnections and network partitions reduce. Routing must cater to two main characteristics of FANETs that reduce the route lifetime. Firstly, the collaboration nature requires the flying nodes to exchange messages and to coordinate among themselves, causing high energy consumption. Secondly, the mobility pattern of the flying nodes is highly dynamic in a three-dimensional space and they may be spaced far apart, causing link disconnection. In this paper, we present a comprehensive survey of the limited research work of routing schemes in FANETs. Different aspects, including objectives, challenges, routing metrics, characteristics, and performance measures, are covered. Furthermore, we present open issues.

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Self-organization based clustering scheme for FANETs using Glowworm Swarm Optimization

Cited by 67 publications

References 24 publications

Bio-Inspired Approaches for Energy-Efficient Localization and Clustering in UAV Networks for Monitoring Wildfires in Remote Areas

Bio-Inspired Approaches for Energy-Efficient Localization and Clustering in UAV Networks for Monitoring Wildfires in Remote Areas

Statistical Analysis of Optimization-Based Clustering Scheme for Multi-UAV Networks

Routing Schemes in FANETs: A Survey

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